End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data

Andrews, Michael; Burkle, Bjorn; Chaudhari, Shravan; DiCroce, Davide; Gleyzer, Sergei; Heintz, Ulrich; Narain, Meenakshi; Paulini, Manfred; Usai, Emanuele

doi:10.1051/epjconf/202125104030

Title: End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data

Abstract

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use lowlevel detector information from the simulated CMS Open Data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves a ROC-AUC score of 0.975±0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier ROC-AUC score increases to 0.9824±0.0013, serving as the first performance benchmark for these CMS Open Data samples.

Authors:

Andrews, Michael ^[1]; Burkle, Bjorn ^[2]; Chaudhari, Shravan ^[3]; DiCroce, Davide ^[4]; Gleyzer, Sergei ^[4]; Heintz, Ulrich ^[2]; Narain, Meenakshi ^[2]; Paulini, Manfred ^[1]; Usai, Emanuele ^[2]

Carnegie Mellon Univ., Pittsburgh, PA (United States)
Brown Univ., Providence, RI (United States)
BITS Pilani, Goa (India)
Univ. of Alabama, Tuscaloosa, AL (United States)

Publication Date:: Mon Aug 23 00:00:00 EDT 2021

Research Org.:: Carnegie Mellon Univ., Pittsburgh, PA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), High Energy Physics (HEP)

OSTI Identifier:: 1837676

Grant/Contract Number:: SC0010118

Resource Type:: Accepted Manuscript

Journal Name:: EPJ Web of Conferences (Online)

Additional Journal Information:: Journal Name: EPJ Web of Conferences (Online); Journal Volume: 251; Journal ID: ISSN 2100-014X

Publisher:: EDP Sciences

Country of Publication:: United States

Language:: English

Subject:: 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats


                    Andrews, Michael, Burkle, Bjorn, Chaudhari, Shravan, DiCroce, Davide, Gleyzer, Sergei, Heintz, Ulrich, Narain, Meenakshi, Paulini, Manfred, and Usai, Emanuele. End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data.  United States: N. p., 2021. 
Web.  doi:10.1051/epjconf/202125104030.

Copy to clipboard


                    Andrews, Michael, Burkle, Bjorn, Chaudhari, Shravan, DiCroce, Davide, Gleyzer, Sergei, Heintz, Ulrich, Narain, Meenakshi, Paulini, Manfred, & Usai, Emanuele. End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data.  United States.  https://doi.org/10.1051/epjconf/202125104030

Copy to clipboard


                    Andrews, Michael, Burkle, Bjorn, Chaudhari, Shravan, DiCroce, Davide, Gleyzer, Sergei, Heintz, Ulrich, Narain, Meenakshi, Paulini, Manfred, and Usai, Emanuele. Mon .  
"End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data".  United States.  https://doi.org/10.1051/epjconf/202125104030.  https://www.osti.gov/servlets/purl/1837676.

Copy to clipboard


                    
@article{osti_1837676,

  title        = {End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data},

  author       = {Andrews, Michael and Burkle, Bjorn and Chaudhari, Shravan and DiCroce, Davide and Gleyzer, Sergei and Heintz, Ulrich and Narain, Meenakshi and Paulini, Manfred and Usai, Emanuele},

  abstractNote = {We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use lowlevel detector information from the simulated CMS Open Data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves a ROC-AUC score of 0.975±0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier ROC-AUC score increases to 0.9824±0.0013, serving as the first performance benchmark for these CMS Open Data samples.},

  doi          = {10.1051/epjconf/202125104030},

  journal      = {EPJ Web of Conferences (Online)},

  number       = ,

  volume       = 251,

  place        = {United States},

  year         = {Mon Aug 23 00:00:00 EDT 2021},

  month        = {Mon Aug 23 00:00:00 EDT 2021}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1051/epjconf/202125104030

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

How to GAN LHC events
journal, January 2019

Butter, Anja; Plehn, Tilman; Winterhalder, Ramon
SciPost Physics, Vol. 7, Issue 6
DOI: 10.21468/SciPostPhys.7.6.075

Energy flow networks: deep sets for particle jets
journal, January 2019

Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
Journal of High Energy Physics, Vol. 2019, Issue 1
DOI: 10.1007/JHEP01(2019)121

End-to-end jet classification of quarks and gluons with the CMS Open Data
journal, October 2020

Andrews, M.; Alison, J.; An, S.
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 977
DOI: 10.1016/j.nima.2020.164304

Samples with full event information including tracker hits for tracking, ML, and top quark tagging studies
dataset, January 2019

Usai, Emanuele; Andrews, Michael; Burkle, Bjorn
CERN Open Data Portal
DOI: 10.7483/opendata.cms.chc3.5kpg

CMS data preservation, re-use and open access policy
dataset, January 2014

Collaboration, Cms
CERN Open Data Portal
DOI: 10.7483/opendata.cms.udbf.jkr9

The anti- k _t jet clustering algorithm
journal, April 2008

Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory
Journal of High Energy Physics, Vol. 2008, Issue 04
DOI: 10.1088/1126-6708/2008/04/063

Deep-learned Top Tagging with a Lorentz Layer
journal, January 2018

Butter, Anja; Kasieczka, Gregor; Plehn, Tilman
SciPost Physics, Vol. 5, Issue 3
DOI: 10.21468/SciPostPhys.5.3.028

Deep-learning top taggers or the end of QCD?
journal, May 2017

Kasieczka, Gregor; Plehn, Tilman; Russell, Michael
Journal of High Energy Physics, Vol. 2017, Issue 5
DOI: 10.1007/JHEP05(2017)006

Similar Records in DOE PAGES and OSTI.GOV collections:

End-to-end jet classification of boosted top quarks with the CMS open data

Journal Article Andrews, M. ; Burkle, B. ; Chen, Y. ; ... - Physical Review. D.

Here we describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique uses low-level detector representation of high-energy collision event as inputs to deep learning algorithms. In this study, we use low-level detector information from the simulated Compact Muon Solenoid (CMS) open data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstratemore »« less
https://doi.org/10.1103/physrevd.105.052008

Full Text Available
End-to-End Pipeline for Trigger Detection on Hit and Track Graphs

Journal Article Xuan, Tingting ; Zhu, Yimin ; Borca-Tasciuc, Giorgian ; ... - Proceedings of the AAAI Conference on Artificial Intelligence

There has been a surge of interest in applying deep learning in particle and nuclear physics to replace labor-intensive offline data analysis with automated online machine learning tasks. This paper details a novel AI-enabled triggering solution for physics experiments in Relativistic Heavy Ion Collider and future Electron-Ion Collider. The triggering system consists of a comprehensive end-to-end pipeline based on Graph Neural Networks that classifies trigger events versus background events, makes online decisions to retain signal data, and enables efficient data acquisition. Here, the triggering system first starts with the coordinates of pixel hits lit up by passing particles in themore »« less
https://doi.org/10.1609/aaai.v37i13.26870

Full Text Available
Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data

Journal Article Andrews, Michael ; Burkle, Bjorn ; Chaudhari, Shravan ; ... - EPJ Web of Conferences (Online)

Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports on the implementation of the end-to-end deep learning with the CMS software framework and the scaling of the end-to-end deep learning with multiple GPUs. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation for particle and event identification. We demonstrate the end-to-end implementation onmore »« less
https://doi.org/10.1051/epjconf/202125103057

Full Text Available
End-to-end jet classification of quarks and gluons with the CMS Open Data

Journal Article Andrews, M. ; Alison, J. ; An, S. ; ... - Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment

We describe the construction of novel end-to-end jet image classifiers to discriminate quark- versus gluon-initiated jets using the simulated CMS Open Data. These multi-detector images correspond to true maps of the low-level energy deposits in the detector, giving the classifiers direct access to the maximum recorded event information about the jet, differing fundamentally from conventional jet images constructed from reconstructed particle-level information. Using this approach, we achieve classification performance competitive with current state-of-the-art jet classifiers that are dominated by particle-based algorithms. We find the performance to be driven by the availability of precise spatial information, highlighting the importance of high-fidelitymore »« less
https://doi.org/10.1016/j.nima.2020.164304
Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain

Journal Article Leng, Shuai ; Yu, Lifeng ; Zhang, Yi ; ... - Medical Physics

Purpose: The purpose of this study was to investigate the correlation between model observer and human observer performance in CT imaging for the task of lesion detection and localization when the lesion location is uncertain.Methods: Two cylindrical rods (3-mm and 5-mm diameters) were placed in a 35 × 26 cm torso-shaped water phantom to simulate lesions with −15 HU contrast at 120 kV. The phantom was scanned 100 times on a 128-slice CT scanner at each of four dose levels (CTDIvol = 5.7, 11.4, 17.1, and 22.8 mGy). Regions of interest (ROIs) around each lesion were extracted to generate imagesmore »« less
https://doi.org/10.1118/1.4812430

Similar Records

Title: End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data

Abstract

Citation Formats

How to GAN LHC events journal, January 2019

Energy flow networks: deep sets for particle jets journal, January 2019

End-to-end jet classification of quarks and gluons with the CMS Open Data journal, October 2020

Samples with full event information including tracker hits for tracking, ML, and top quark tagging studies dataset, January 2019

CMS data preservation, re-use and open access policy dataset, January 2014

The anti- k t jet clustering algorithm journal, April 2008

Deep-learned Top Tagging with a Lorentz Layer journal, January 2018

Deep-learning top taggers or the end of QCD? journal, May 2017

How to GAN LHC events
journal, January 2019

Energy flow networks: deep sets for particle jets
journal, January 2019

End-to-end jet classification of quarks and gluons with the CMS Open Data
journal, October 2020

Samples with full event information including tracker hits for tracking, ML, and top quark tagging studies
dataset, January 2019

CMS data preservation, re-use and open access policy
dataset, January 2014

The anti- k _t jet clustering algorithm
journal, April 2008

Deep-learned Top Tagging with a Lorentz Layer
journal, January 2018

Deep-learning top taggers or the end of QCD?
journal, May 2017